Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn't touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence. But mastering machine learning is a difficult process.

Here is my python source code for training an agent to play contra nes. By using Proximal Policy Optimization (PPO) algorithm introduced in the paper Proximal Policy Optimization Algorithms paper. For your information, PPO is the algorithm proposed by OpenAI and used for training OpenAI Five, which is the first AI to beat the world champions in an esports game. Specifically, The OpenAI Five dispatched a team of casters and ex-pros with MMR rankings in the 99.95th percentile of Dota 2 players in August 2018. It has been a while since I have released my A3C implementation (A3C code) and PPO implementation (PPO code) for training an agent to play super mario bros.

Artificial Intelligence (AI) is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, this is just the start of the AI revolution. The field of AI, especially deep learning, is still in its infancy with tremendous opportunity for exploration and improvement. For instance, deep neural networks of today are rapidly growing in size and use too much memory, compute, and energy. To make AI truly ubiquitous, it needs to run on the end device within a tight power and thermal budget.

I was just a youth when Evelyn Wood debuted her speed-reading course back in 1959. For years, I was fascinated with the prospect of getting my reading assignments over with as quickly as possible so that I could get on to the fun part of life. Fortunately, I massively turned that around. The Evelyn Wood Reading Dynamics course became a huge sensation. So much so that the Kennedy White House sent staff members to take the course.

This had the effect of bottlenecking deep learning, limiting it to the few projects that met those conditions. Over the last couple years, however, things have changed. The driver behind this growth is transfer learning. Transfer learning, broadly, is the idea that the knowledge accumulated in a model trained for a specific task--say, identifying flowers in a photo--can be transferred to another model to assist in making predictions for a different, related task--like identifying melanomas on someone's skin. Note: If you want a more technical dive into transfer learning, Sebastian Ruder has written a fantastic primer.

Machine Learning works on the principles of computer algorithms that learn in a reflex manner through trials and experiences. It is an application of Artificial Intelligence that permits program applications to anticipate results with utmost precision. It makes a distinction to create computer programs and to assist computers to memorize without human intercession. The future of machine learning is exceptionally exciting. At present, almost every common domain is powered by machine learning applications.

Artificial intelligence – AI – was a mere computational theory back in the 1950s when Alan Turing designed the first Turing Test to measure a machine's intelligence. Today, AI inhabits consumer electronics in the form of Siri, Cortana, Alexa and Google Assistant – it lives behind our internet browsers, within the relative confines of wireless networks and circuit boards. We interact with AI all the time – Google's auto-suggest function, customer service bots and YouTube's search algorithm are all examples of AI. In just half a century, AI's role in society has become firmly established. Developments in software programming and IT have facilitated important innovations in AI.

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically-optimized, functionally graded structures, particularly when those structures are represented as discrete density maps. One na\"ive approach to creating variable-density cellular structures is simply replacing the discrete density map with an unselective type of unit cells having corresponding densities. However, doing so breaks the desired mechanical behavior, as equivalent density alone does not guarantee equivalent mechanical properties. Another approach uses homogenization methods to estimate each pre-defined unit cell's effective properties and remaps the unit cells following a scaling law. However, a scaling law merely mitigates the problem by performing an indirect and inaccurate mapping from the material property space to single-type unit cells. In contrast, we propose a deep generative model that resolves this problem by automatically learning an accurate mapping and generating diverse cellular unit cells conditioned on desired properties (i.e., Young's modulus and Poisson's ratio). We demonstrate our method via the use of implicit function-based unit cells and conditional generative adversarial networks. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (relative error <5%), 2) create functionally graded cellular structures with high-quality interface connectivity (98.7% average overlap area at interfaces), and 3) improve the structural performance over the conventional topology-optimized variable-density structure (84.4% reduction in concentrated stress and extra 7% reduction in displacement).

Machine learning has achieved tremendous success in a variety of domains in recent years. However, a lot of these success stories have been in places where the training and the testing distributions are extremely similar to each other. In everyday situations when models are tested in slightly different data than they were trained on, ML algorithms can fail spectacularly. This research attempts to formally define this problem, what sets of assumptions are reasonable to make in our data and what kind of guarantees we hope to obtain from them. Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable (when using them to predict) regardless of their context, and out of distribution generalization.

Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is performed to treat knee-joint ailment. Lack of visual information of the surgical site obtained from miniaturized cameras make this surgical procedure more complex. Knee cavity is a very confined space; therefore, surgical scenes are captured at close proximity. Insignificant context of knee atlas often makes them unrecognizable as a consequence unintentional tissue damage often occurred and shows a long learning curve to train new surgeons. Automatic context awareness through labeling of the surgical site can be an alternative to mitigate these drawbacks. However, from the previous studies, it is confirmed that the surgical site exhibits several limitations, among others, lack of discriminative contextual information such as texture and features which drastically limits this vision task. Additionally, poor imaging conditions and lack of accurate ground-truth labels are also limiting the accuracy. To mitigate these limitations of knee arthroscopy, in this work we proposed a scene segmentation method that successfully segments multi structures.